Breadcrumb

 
 

Appearance based object recognition using independent component analysis

Title:

Appearance based object recognition using independent component analysis

Sahambi, Harkirat S (2000) Appearance based object recognition using independent component analysis. Masters thesis, Concordia University.

[img]
Preview
PDF
3555Kb

Abstract

In recent years there has been a renewed interest in appearance based 3-dimensional object recognition. All possible views of an object define its workspace, known as visual workspace. This workspace is coarsely sampled and projected onto a lower-dimensional space and represented as workspace manifold. The dimensionality reduction is usually done using Karhunen-Loeve transform (KLT), or principal component analysis (PCA). The lower dimensional space has been called the eigenspace. For recognition, the test image is projected likewise onto the eigenspace and its position on the appearance manifold is used for the recognition phase. In object recognition, there are many situations in which features based on only second order statistics are not sufficient. To take into account higher order statistics, Independent Component Analysis (ICA) has been proposed. This thesis presents results on appearance based 3-dimensional object recognition accomplished by using ICA. (Abstract shortened by UMI.)

Divisions:Concordia University > Faculty of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Sahambi, Harkirat S
Pagination:xi, 91 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:Theses (M.A.Sc.)
Program:Electrical and Computer Engineering
Date:2000
Thesis Supervisor(s):Khorasani, Khashayar
ID Code:1232
Deposited By:Concordia University Libraries
Deposited On:27 Aug 2009 13:17
Last Modified:08 Dec 2010 10:19
Related URLs:
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Document Downloads

More statistics for this item...

Concordia University - Footer